Planned subgoals.
While it is hard to directly visualize subgoals planned in the learned lossy representation space, we analyze their characteristics in Fig. REF . Same as in Sec. REF . we project image observations in the target scene using t-SNE [1]}. A trajectory of executing the real-world Task B using the fine-tuned policy it visualized on this t-SNE plot. The orange dots mark the computed lossy representations of the initial state \(s_0\)  and the final goal \(g\)  of the task. The subgoals planned in the lossy representation space \(\hat{z}_1, ..., \hat{z}_3\)  are shown as blue dots on the plot. The observed images at each step during task execution are plotted as the blue curve. Although the lossy representations cannot be directly reconstructed to the image space, we compute their distances to the computed representations of the observed images at each step. The closest observation to each planned subgoal is shown on the plot. As we can see, the planned subgoals are close to the executed trajectory. The first subgoal commands the robot to reach to the colander. The second subgoal commands the robot to place the colander onto the stove on the left. In the third subgoal, the robot gripper aims to pick up the toy from the table.
